Title :
Research on power load forecasting based on combined model of Markov and BP neural networks
Author :
Niu, Dongxiao ; Shi, Hui ; Li, Jianqing ; Xu, Cong
Author_Institution :
Sch. of Bus. Manage., Univ. of North China Electr. Power, Beijing, China
Abstract :
With the development of power systems, the accuracy of electric power load forecasting plays a more important role in the safe operation of power system and raising the level of the national economy. Load forecasting is a very important element of the power system operation scheduling, an important module of the energy management system (EMS), and is the basis to ensure safe and economical operation and achieve grid scientific management and scheduling. Whether Power load forecast is accurate will also affect power system planning, programming and other management departments´ works. This paper selected the point load data of some city, used BP neural network training, network simulation and prediction, and through the amendment model of Markov to error-correction and adjust it will further enhance point load forecasting accuracy based on the BP neural network prediction. Through point load forecasting accurately we got more accurate information for planning and operation of power system electricity generation and distribution.
Keywords :
Markov processes; backpropagation; load forecasting; load management; neural nets; power grids; power system planning; power system simulation; socio-economic effects; BP neural network; Markov model; electric power load forecasting; electricity distribution; electricity generation; energy management system; point load forecasting; power system operation scheduling; Artificial neural networks; Forecasting; Load forecasting; Load modeling; Markov processes; Predictive models; BP neural network; Markov model; power load forecasting;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554042